SoroushVahidi/scidocs huggingface dataset:
Adding Chinese Captions to Images
(2016)

This paper extends research on automated image captioning in the dimension of language, studying how to generate Chinese sentence descriptions for unlabeled images. To evaluate image captioning in this novel context, we present Flickr8k-CN, a bilingual extension of the popular Flickr8k set. The new multimedia dataset can be used to quantitatively assess the performance of Chinese captioning and English-Chinese machine translation. The possibility of re-using existing English data and models via machine translation is investigated. Our study reveals to some extent that a computer can master two distinct languages, English and Chinese, at a similar level for describing the visual world. Data is publicly available at http://tinyurl.com/flickr8kcn
MAGAN: Margin Adaptation for Generative Adversarial Networks
(2017)

We propose a novel training procedure for Generative Adversarial Networks (GANs) to improve stability and performance by using an adaptive hinge loss objective function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive both a principled criterion for updating the margin and an approximate convergence measure. The resulting training procedure is simple yet robust on a diverse set of datasets. We evaluate the proposed training procedure on the task of unsupervised image generation, noting both qualitative and quantitative performance improvements.
Efficient and fast multi-modal foreground-background segmentation using RGBD data
(2017)

This paper addresses the problem of foreground and background segmentation. Multi-modal data specifically RGBD data has gain many tasks in computer vision recently. However, techniques for background subtraction based only on single-modality still report state-of-the-art results in many benchmarks. Succeeding the fusion of depth and color data for this task requires a robust formalization allowing at the same time higher precision and faster processing. To this end, we propose to make use of kernel density estimation technique to adapt multi-modal data. To speed up kernel density estimation, we explore the fast Gauss transform which allows the summation of a mixture of M kernel at N evaluation points in O(M+N) time as opposed to O(MN) time for a direct evaluation. Extensive experiments have been carried out on four publicly available RGBD foreground/background datasets. Results demonstrate that our proposal outperforms state-of-the-art methods for almost all of the sequences acquired in challenging indoor and outdoor contexts with a fast and non-parametric operation. c © 2017 Elsevier Ltd. All rights reserved.
Semi-Supervised Ensemble Ranking
(2008)

Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with these approaches is that the learned weights for ranking algorithms are query independent. This is suboptimal since a ranking algorithm could perform well for certain queries but poorly for others. In this paper, we propose a novel semi-supervised ensemble ranking (SSER) algorithm that learns query-dependent weights when combining multiple rankers in document retrieval. The proposed SSER algorithm is formulated as an SVM-like quadratic program (QP), and therefore can be solved efficiently by taking advantage of optimization techniques that were widely used in existing SVM solvers. We evaluated the proposed technique on a standard document retrieval testbed and observed encouraging results by comparing to a number of state-of-the-art techniques.
Comparative evaluation of text- and citation-based plagiarism detection approaches using guttenplag
(2011)

Various approaches for plagiarism detection exist. All are based on more or less sophisticated text analysis methods such as string matching, fingerprinting or style comparison. In this paper a new approach called Citation-based Plagiarism Detection is evaluated using a doctoral thesis, in which a volunteer crowd-sourcing project called GuttenPlag identified substantial amounts of plagiarism through careful manual inspection. This new approach is able to identify similar and plagiarized documents based on the citations used in the text. It is shown that citation-based plagiarism detection performs significantly better than text-based procedures in identifying strong paraphrasing, translation and some idea plagiarism. Detection rates can be improved by combining citation-based with text-based plagiarism detection.
Routing, scheduling and channel assignment in Wireless Mesh Networks: Optimization models and algorithms
(2010)

Wireless Mesh networks (WMNs) can partially replace the wired backbone of traditional wireless access networks and, similarly, they require to carefully plan radio resource assignment in order to provide the same quality guarantees to traffic flows. In this paper we study the radio resource assignment optimization problem in Wireless Mesh Networks assuming a time division multiple access (TDMA) scheme, a dynamic power control able to vary emitted power slot-by-slot, and a rate adaptation mechanism that sets transmission rates according to the signalto-interference-and-noise ratio (SINR). The proposed optimization framework includes routing, scheduling and channel assignment. Quality requirements of traffic demands are expressed in terms of minimum bandwidth and modeled with constraints defining the number of information units (packets) that must be delivered per frame. We consider an alternative problem formulation where decision variables represent compatible sets of links active in the same slot and channel, called configurations. We propose a two phases solution approach where a set of configurations is first selected to meet traffic requirements along the best available paths, and then configurations are assigned to channels according to device characteristics and constraints. The optimization goal is to minimize the number of used slots, which is directly related to the global resource allocation efficiency. We provide a lower bound of the optimal solution solving the continuous relaxation of problem formulation. Moreover, we propose a heuristic approach to determine practical integer solutions (upper bound). Since configuration variables are exponentially many, our solution approaches are based on the Column Generation technique. In order to assess the effectiveness of the proposed algorithms we show the numerical results obtained on a set of realistic-size randomly generated instances.
A high performance 76.5 GHz FMCW RADAR for advanced driving assistance system
(2016)

Frequency modulated Continuous Wave (FMCW) is continuous wave RADAR widely used for Intelligent Adaptive Cruise Control (ACC) and Collision Warning System (CWS). The paper presents a design and simulation of millimeter wave FMCW RADAR. Paper introduces a mathematical modeling of a FMCW RADAR including its target model. Design of a 76.5 GHz FMCW RADAR & its target model by using Advanced Design System (ADS) software and target range & velocity at various conditions are employed in the simulated FMCW RADAR system to verify the operation of the system.
CANAuth - A Simple, Backward Compatible Broadcast Authentication Protocol for CAN bus
(2011)

The Controller-Area Network (CAN) bus protocol [1] is a bus protocol invented in 1986 by Robert Bosch GmbH, originally intended for automotive use. By now, the bus can be found in devices ranging from cars and trucks, over lightning setups to industrial looms. Due to its nature, it is a system very much focused on safety, i.e., reliability. Unfortunately, there is no build-in way to enforce security, such as encryption or authentication. In this paper, we investigate the problems associated with implementing a backward compatible message authentication protocol on the CAN bus. We show which constraints such a protocol has to meet and why this eliminates, to the best of our knowledge, all the authentication protocols published so far. Furthermore, we present a message authentication protocol, CANAuth, that meets all of the requirements set forth and does not violate any constraint of the CAN bus. Keywords—CAN bus, embedded networks, broadcast authentication, symmetric cryptography
Finding social roles in Wikipedia
(2011)

This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential "structural signatures" that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month's cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.
Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles
(2016)

Magnetic anomaly detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.
Demand Side Management in Smart Grid Using Heuristic Optimization
(2012)

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Cookie Hijacking in the Wild : Security and Privacy Implications
(2016)

The widespread demand for online privacy, also fueled by widely-publicized demonstrations of session hijacking attacks against popular websites, has spearheaded the increasing deployment of HTTPS. However, many websites still avoid ubiquitous encryption due to performance or compatibility issues. The prevailing approach in these cases is to force critical functionality and sensitive data access over encrypted connections, while allowing more innocuous functionality to be accessed over HTTP. In practice, this approach is prone to flaws that can expose sensitive information or functionality to third parties. In this work, we conduct an in-depth assessment of a diverse set of major websites and explore what functionality and information is exposed to attackers that have hijacked a user’s HTTP cookies. We identify a recurring pattern across websites with partially deployed HTTPS; service personalization inadvertently results in the exposure of private information. The separation of functionality across multiple cookies with different scopes and inter-dependencies further complicates matters, as imprecise access control renders restricted account functionality accessible to non-session cookies. Our cookie hijacking study reveals a number of severe flaws; attackers can obtain the user’s home and work address and visited websites from Google, Bing and Baidu expose the user’s complete search history, and Yahoo allows attackers to extract the contact list and send emails from the user’s account. Furthermore, e-commerce vendors such as Amazon and Ebay expose the user’s purchase history (partial and full respectively), and almost every website exposes the user’s name and email address. Ad networks like Doubleclick can also reveal pages the user has visited. To fully evaluate the practicality and extent of cookie hijacking, we explore multiple aspects of the online ecosystem, including mobile apps, browser security mechanisms, extensions and search bars. To estimate the extent of the threat, we run IRB-approved measurements on a subset of our university’s public wireless network for 30 days, and detect over 282K accounts exposing the cookies required for our hijacking attacks. We also explore how users can protect themselves and find that, while mechanisms such as the EFF’s HTTPS Everywhere extension can reduce the attack surface, HTTP cookies are still regularly exposed. The privacy implications of these attacks become even more alarming when considering how they can be used to deanonymize Tor users. Our measurements suggest that a significant portion of Tor users may currently be vulnerable to cookie hijacking.
Learning Profiles in Duplicate Question Detection
(2017)

This paper presents the results of systematic and comparative experimentation with major types of methodologies for automatic duplicate question detection when these are applied to datasets of progressively larger sizes, thus allowing to study the learning profiles of this task under these different approaches and evaluate their merits. This study was made possible by resorting to the recent release for research purposes, by the Quora online question answering engine, of a new dataset with over 400,000 pairs labeled with respect to their elements being duplicate interrogative segments.
Neurobiology of Schizophrenia
(2006)

With its hallucinations, delusions, thought disorder, and cognitive deficits, schizophrenia affects the most basic human processes of perception, emotion, and judgment. Evidence increasingly suggests that schizophrenia is a subtle disorder of brain development and plasticity. Genetic studies are beginning to identify proteins of candidate genetic risk factors for schizophrenia, including dysbindin, neuregulin 1, DAOA, COMT, and DISC1, and neurobiological studies of the normal and variant forms of these genes are now well justified. We suggest that DISC1 may offer especially valuable insights. Mechanistic studies of the properties of these candidate genes and their protein products should clarify the molecular, cellular, and systems-level pathogenesis of schizophrenia. This can help redefine the schizophrenia phenotype and shed light on the relationship between schizophrenia and other major psychiatric disorders. Understanding these basic pathologic processes may yield novel targets for the development of more effective treatments.
The design and implementation of microdrivers
(2008)

Device drivers commonly execute in the kernel to achieve high performance and easy access to kernel services. However, this comes at the price of decreased reliability and increased programming difficulty. Driver programmers are unable to use user-mode development tools and must instead use cumbersome kernel tools. Faults in kernel drivers can cause the entire operating system to crash. User-mode drivers have long been seen as a solution to this problem, but suffer from either poor performance or new interfaces that require a rewrite of existing drivers.
 This paper introduces the Microdrivers architecture that achieves high performance and compatibility by leaving critical path code in the kernel and moving the rest of the driver code to a user-mode process. This allows data-handling operations critical to I/O performance to run at full speed, while management operations such as initialization and configuration run at reduced speed in user-level. To achieve compatibility, we present DriverSlicer, a tool that splits existing kernel drivers into a kernel-level component and a user-level component using a small number of programmer annotations. Experiments show that as much as 65% of driver code can be removed from the kernel without affecting common-case performance, and that only 1-6 percent of the code requires annotations.
XFI: Software Guards for System Address Spaces
(2006)

XFI is a comprehensive protection system that offers both flexible access control and fundamental integrity guarantees, at any privilege level and even for legacy code in commodity systems. For this purpose, XFI combines static analysis with inline software guards and a two-stack execution model. We have implemented XFI for Windows on the x86 architecture using binary rewriting and a simple, stand-alone verifier; the implementation's correctness depends on the verifier, but not on the rewriter. We have applied XFI to software such as device drivers and multimedia codecs. The resulting modules function safely within both kernel and user-mode address spaces, with only modest enforcement overheads.
CuriOS: Improving Reliability through Operating System Structure
(2008)

An error that occurs in a microkernel operating system service can potentially result in state corruption and service failure. A simple restart of the failed service is not always the best solution for reliability. Blindly restarting a service which maintains client-related state such as session information results in the loss of this state and affects all clients that were using the service. CuriOS represents a novel OS design that uses lightweight distribution, isolation and persistence of OS service state to mitigate the problem of state loss during a restart. The design also significantly reduces error propagation within client-related state maintained by an OS service. This is achieved by encapsulating services in separate protection domains and granting access to client-related state only when required for request processing. Fault injection experiments show that it is possible to recover from between 87% and 100% of manifested errors in OS services such as the file system, network, timer and scheduler while maintaining low performance overheads.
CIL: Intermediate Language and Tools for Analysis and Transformation of C Programs
(2002)

This paper describes the C Intermediate Language: a highlevel representation along with a set of tools that permit easy analysis and source-to-source transformation of C programs. Compared to C, CIL has fewer constructs. It breaks down certain complicated constructs of C into simpler ones, and thus it works at a lower level than abstract-syntax trees. But CIL is also more high-level than typical intermediate languages (e.g., three-address code) designed for compilation. As a result, what we have is a representation that makes it easy to analyze and manipulate C programs, and emit them in a form that resembles the original source. Moreover, it comes with a front-end that translates to CIL not only ANSI C programs but also those using Microsoft C or GNU C extensions. We describe the structure of CIL with a focus on how it disambiguates those features of C that we found to be most confusing for program analysis and transformation. We also describe a whole-program merger based on structural type equality, allowing a complete project to be viewed as a single compilation unit. As a representative application of CIL, we show a transformation aimed at making code immune to stack-smashing attacks. We are currently using CIL as part of a system that analyzes and instruments C programs with run-time checks to ensure type safety. CIL has served us very well in this project, and we believe it can usefully be applied in other situations as well.
Thorough static analysis of device drivers
(2006)

Bugs in kernel-level device drivers cause 85% of the system crashes in the Windows XP operating system [44]. One of the sources of these errors is the complexity of the Windows driver API itself: programmers must master a complex set of rules about how to use the driver API in order to create drivers that are good clients of the kernel. We have built a static analysis engine that finds API usage errors in C programs. The Static Driver Verifier tool (SDV) uses this engine to find kernel API usage errors in a driver. SDV includes models of the OS and the environment of the device driver, and over sixty API usage rules. SDV is intended to be used by driver developers "out of the box." Thus, it has stringent requirements: (1) complete automation with no input from the user; (2) a low rate of false errors. We discuss the techniques used in SDV to meet these requirements, and empirical results from running SDV on over one hundred Windows device drivers.
Why We Need New Evaluation Metrics for NLG
(2017)

The majority of NLG evaluation relies on automatic metrics, such as BLEU. In this paper, we motivate the need for novel, systemand data-independent automatic evaluation methods: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG. We also show that metric performance is dataand system-specific. Nevertheless, our results also suggest that automatic metrics perform reliably at system-level and can support system development by finding cases where a system performs poorly.